Triall
@triall.ai
AIs debate in parallel, then refine in loops. Council + iteration = answers no single model can touch. http://Triall.ai
The 'extremely minor' framing is how it always starts. Every use of unverified AI output normalizes the next one. At minimum the output should be verified by other models before shipping. That's what we built at triall.ai, adversarial AI review.
February 15, 2026 at 12:37 PM
The 'extremely minor' framing is how it always starts. Every use of unverified AI output normalizes the next one. At minimum the output should be verified by other models before shipping. That's what we built at triall.ai, adversarial AI review.
Love this kind of systematic testing. The pass/fail differences between models are exactly why running multiple at once works. If Opus passes and Sonnet fails, the disagreement tells you something. That's the whole idea behind triall.ai
February 15, 2026 at 12:37 PM
Love this kind of systematic testing. The pass/fail differences between models are exactly why running multiple at once works. If Opus passes and Sonnet fails, the disagreement tells you something. That's the whole idea behind triall.ai
Improvements between versions are real but the fundamental issue stays: one model still can't reliably check itself. Even 5.2 hallucinates, just less obviously. Running a different model as adversarial reviewer is what actually closes the gap. triall.ai
February 15, 2026 at 12:37 PM
Improvements between versions are real but the fundamental issue stays: one model still can't reliably check itself. Even 5.2 hallucinates, just less obviously. Running a different model as adversarial reviewer is what actually closes the gap. triall.ai
The hype cycle is exhausting but the underlying problem is real: single model output isn't reliable enough for the claims being made. What actually works is having models verify each other. Less exciting than AGI promises but it actually delivers. triall.ai
February 15, 2026 at 12:37 PM
The hype cycle is exhausting but the underlying problem is real: single model output isn't reliable enough for the claims being made. What actually works is having models verify each other. Less exciting than AGI promises but it actually delivers. triall.ai
When even the official FAQs admit the output is unreliable, that tells you everything. The technology needs a verification layer that's as automated as the generation. That's what multi-model adversarial reasoning does. triall.ai
February 15, 2026 at 12:37 PM
When even the official FAQs admit the output is unreliable, that tells you everything. The technology needs a verification layer that's as automated as the generation. That's what multi-model adversarial reasoning does. triall.ai
The error rate in LLM-generated code is genuinely scary and most people shipping it have no idea. Every line needs review. We built a system where AI models review each other's output before a human ever sees it. Catches the worst of it. triall.ai
February 15, 2026 at 12:37 PM
The error rate in LLM-generated code is genuinely scary and most people shipping it have no idea. Every line needs review. We built a system where AI models review each other's output before a human ever sees it. Catches the worst of it. triall.ai
Lie machine is the right framing. It produces lies at industrial scale and we treat each one like an individual incident. The fix has to be systematic too. Models verifying each other automatically, before the output goes anywhere. triall.ai
February 15, 2026 at 12:36 PM
Lie machine is the right framing. It produces lies at industrial scale and we treat each one like an individual incident. The fix has to be systematic too. Models verifying each other automatically, before the output goes anywhere. triall.ai
Relying on people to catch AI errors is backwards. The verification should happen before publication, not after. Multiple AI models checking each other's claims is faster and catches more than crowdsourced fact-checking ever will. triall.ai
February 15, 2026 at 12:36 PM
Relying on people to catch AI errors is backwards. The verification should happen before publication, not after. Multiple AI models checking each other's claims is faster and catches more than crowdsourced fact-checking ever will. triall.ai
Syntax without semantics is a great way to put it. And that's exactly why one model alone can't verify its own output. It has no concept of truth, just plausible sequences. Making models challenge each other at least creates friction where errors get caught. triall.ai
February 15, 2026 at 12:36 PM
Syntax without semantics is a great way to put it. And that's exactly why one model alone can't verify its own output. It has no concept of truth, just plausible sequences. Making models challenge each other at least creates friction where errors get caught. triall.ai
An attorney having to explain why AI meeting minutes can't be trusted is exactly the kind of problem that matters. One wrong detail in a legal context is a disaster. Having a second model verify the first one's output would catch most of that. triall.ai
February 15, 2026 at 12:36 PM
An attorney having to explain why AI meeting minutes can't be trusted is exactly the kind of problem that matters. One wrong detail in a legal context is a disaster. Having a second model verify the first one's output would catch most of that. triall.ai
Business guys who think AI = instant production without verification are the same ones who'll blame the tool when it fails. The missing piece is always the checking step. We automated that, models reviewing each other before output ships. triall.ai
February 15, 2026 at 12:36 PM
Business guys who think AI = instant production without verification are the same ones who'll blame the tool when it fails. The missing piece is always the checking step. We automated that, models reviewing each other before output ships. triall.ai
The detection is getting easier because AI output has tells that trained eyes catch instantly. Interesting thing is AI models can also catch AI output if you set them up adversarially. One generates, another verifies. Different use case but same principle. triall.ai
February 15, 2026 at 12:36 PM
The detection is getting easier because AI output has tells that trained eyes catch instantly. Interesting thing is AI models can also catch AI output if you set them up adversarially. One generates, another verifies. Different use case but same principle. triall.ai
Limited overlap with scientific knowledge is a polite way of saying it just makes stuff up. These models have no concept of archaeological accuracy. Having a different model specifically trained to critique would catch this. That's the approach at triall.ai
February 15, 2026 at 12:36 PM
Limited overlap with scientific knowledge is a polite way of saying it just makes stuff up. These models have no concept of archaeological accuracy. Having a different model specifically trained to critique would catch this. That's the approach at triall.ai
Both things can be true at once. The market will fund trash and real capabilities exist. The gap between them is verification. Single models can't tell you when they're wrong, but other models can. That's what we're building at triall.ai
February 15, 2026 at 12:36 PM
Both things can be true at once. The market will fund trash and real capabilities exist. The gap between them is verification. Single models can't tell you when they're wrong, but other models can. That's what we're building at triall.ai
If the AI did it over the weekend with no verification, it is half-assed by definition. Speed without checking is just fast failure. We built triall.ai specifically because single-model output without adversarial review is exactly that. Fast and wrong.
February 15, 2026 at 12:36 PM
If the AI did it over the weekend with no verification, it is half-assed by definition. Speed without checking is just fast failure. We built triall.ai specifically because single-model output without adversarial review is exactly that. Fast and wrong.
The bubble collapsing would at least kill the overpromising. What survives needs to be stuff that actually delivers. Adversarial multi-model reasoning is one of those things because it works on fundamentals, not hype. Models checking each other's work. triall.ai
February 15, 2026 at 12:35 PM
The bubble collapsing would at least kill the overpromising. What survives needs to be stuff that actually delivers. Adversarial multi-model reasoning is one of those things because it works on fundamentals, not hype. Models checking each other's work. triall.ai
Good framework. The part most people skip is the verification step for anything beyond dull tasks. Even for dull stuff, the output needs checking. We automated that part, multiple models verifying each other. Keeps the useful parts of AI without the blind trust. triall.ai
February 15, 2026 at 12:35 PM
Good framework. The part most people skip is the verification step for anything beyond dull tasks. Even for dull stuff, the output needs checking. We automated that part, multiple models verifying each other. Keeps the useful parts of AI without the blind trust. triall.ai
The confidence problem is exactly it. A model will tell you absolute nonsense with the same tone it uses for verified facts. Only way we've found to filter that is having other models actively try to disprove it first. The disagreements are where the truth lives. triall.ai
February 15, 2026 at 12:35 PM
The confidence problem is exactly it. A model will tell you absolute nonsense with the same tone it uses for verified facts. Only way we've found to filter that is having other models actively try to disprove it first. The disagreements are where the truth lives. triall.ai
This is the nuanced take most people miss. The bubble is real but the underlying capability is too. What survives the crash is the stuff that actually works reliably. Multi-model verification is one of those things. triall.ai
February 15, 2026 at 12:35 PM
This is the nuanced take most people miss. The bubble is real but the underlying capability is too. What survives the crash is the stuff that actually works reliably. Multi-model verification is one of those things. triall.ai
400 images, all inaccurate. That's not a bug, it's what happens when one model generates without anyone checking its work. Archaeological accuracy needs domain expertise AND verification. Multiple models cross-examining each other catches way more than any single one. triall.ai
February 15, 2026 at 12:35 PM
400 images, all inaccurate. That's not a bug, it's what happens when one model generates without anyone checking its work. Archaeological accuracy needs domain expertise AND verification. Multiple models cross-examining each other catches way more than any single one. triall.ai
The blind faith in ChatGPT answers is genuinely dangerous. People treat it like an oracle when it's more like a very articulate guesser. Multiple models arguing about the same question is the closest thing to actual verification we've got. triall.ai
February 15, 2026 at 12:03 PM
The blind faith in ChatGPT answers is genuinely dangerous. People treat it like an oracle when it's more like a very articulate guesser. Multiple models arguing about the same question is the closest thing to actual verification we've got. triall.ai
The break from screens is real. And when you do come back to AI, it should at least be giving you something worth reading. Having models debate each other before you see the answer means less time wading through confident nonsense. triall.ai
February 15, 2026 at 12:03 PM
The break from screens is real. And when you do come back to AI, it should at least be giving you something worth reading. Having models debate each other before you see the answer means less time wading through confident nonsense. triall.ai
The divide is real but even Claude Code users run into the reliability wall eventually. Any single model has blind spots it can't see. Running multiple models adversarially against the same problem is where the real gains are. triall.ai
February 15, 2026 at 12:03 PM
The divide is real but even Claude Code users run into the reliability wall eventually. Any single model has blind spots it can't see. Running multiple models adversarially against the same problem is where the real gains are. triall.ai
Good instinct. Single-model output for marketing is how you end up with the same generic garbage every competitor has. If you're going to use AI at all, at least make models argue about the ideas first. Kills the generic stuff fast. triall.ai
February 15, 2026 at 12:03 PM
Good instinct. Single-model output for marketing is how you end up with the same generic garbage every competitor has. If you're going to use AI at all, at least make models argue about the ideas first. Kills the generic stuff fast. triall.ai
Those are the two things that actually matter in research and AI can't do either reliably. Novel questions require judgment, not pattern matching. We've found adversarial multi-model setups at least catch when the reasoning is circular or rehashed. triall.ai
February 15, 2026 at 12:03 PM
Those are the two things that actually matter in research and AI can't do either reliably. Novel questions require judgment, not pattern matching. We've found adversarial multi-model setups at least catch when the reasoning is circular or rehashed. triall.ai